{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node Representation Learning with attri2vec on Citeseer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the python implementation of the attri2vec algorithm outlined in paper ***[Attributed Network Embedding Via Subspace Discovery](https://arxiv.org/abs/1901.04095)*** D. Zhang, Y. Jie, X. Zhu and C. Zhang, arXiv:1901.04095, [cs.SI], 2019. The implementation uses the stellargraph libraries.\n",
    "\n",
    "\n",
    "## attri2vec\n",
    "\n",
    "attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [`DeepWalk`](https://dl.acm.org/citation.cfm?id=2623732)/[`node2vec`](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace, which is achieved by maximizing the occurrence probability of context nodes conditioned on the representation of the target nodes. The probability is modelled by Softmax and negative sampling is used to speed up its calculation. This makes attri2vec equivalent to predict whether a node occurs in the given target node's context in random walks with the representation of the target node, by minimizing the cross-entropy loss. \n",
    "\n",
    "In implementation, node embeddings are learnt by solving a simple classification task: given a large set of \"positive\" `(target, context)` node pairs generated from random walks performed on the graph (i.e., node pairs that co-occur within a certain context window in random walks), and an equally large set of \"negative\" node pairs that are randomly selected from the graph according to a certain distribution, learn a binary classifier that predicts whether arbitrary node pairs are likely to co-occur in a random walk performed on the graph. Through learning this simple binary node-pair-classification task, the model automatically learns an inductive mapping from attributes of nodes to node embeddings in a low-dimensional vector space, which preserves structural and feature similarities of the nodes. \n",
    "\n",
    "To train the attri2vec model, we first construct a training set of nodes, which is composed of an equal number of positive and negative `(target, context)` pairs from the graph. The positive `(target, context)` pairs are the node pairs co-occurring on random walks over the graph whereas the negative node pairs are the sampled randomly from the global node degree distribution of the graph. In attri2vec, each node is attached with two kinds of embeddings: 1) the inductive 'input embedding', i.e, the objective embedding, obtained by perform a linear/non-linear transformation on node content features, and 2) 'output embedding', i.e., the parameter vector used to predict its occurrence as a context node, obtained by looking up a parameter table. Given a `(target, context)` pair, attri2vec outputs a predictive value to indicate whether it is positive or negative, which is obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation. \n",
    "\n",
    "The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
    "from stellargraph.layer import Attri2Vec, link_classification\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "from pandas.core.indexes.base import Index\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.CiteSeer()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load edges in order 'cited-paper' <- 'citing-paper'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "citeseer_location = os.path.join(dataset.data_directory, \"citeseer.cites\")\n",
    "g_nx = nx.read_edgelist(path=citeseer_location, create_using=nx.DiGraph()).reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the graph to undirected graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx = g_nx.to_undirected()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the node attribute data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "citeseer_data_location = os.path.join(dataset.data_directory, \"citeseer.content\")\n",
    "attr_names = [\"w_{}\".format(ii) for ii in range(3703)]\n",
    "node_column_names = attr_names + [\"subject\"]\n",
    "node_attr = pd.read_csv(\n",
    "    citeseer_data_location, sep=\"\\t\", header=None, names=node_column_names\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the type of the indexes of node_attr to str."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_attr.index = Index(list(map(str, list(node_attr.index))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original graph contains some nodes with no attributes. We remove them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx = g_nx.subgraph(list(node_attr.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the largest connected component. For clarity we ignore isolated nodes and subgraphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Largest subgraph statistics: 2110 nodes, 3720 edges\n"
     ]
    }
   ],
   "source": [
    "g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
    "g_nx = max(g_nx_ccs, key=len)\n",
    "print(\n",
    "    \"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
    "        g_nx.number_of_nodes(), g_nx.number_of_edges()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify node and edge types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(g_nx, \"paper\", \"label\")\n",
    "nx.set_edge_attributes(g_nx, \"cites\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the ids of the nodes in the selected largest connected component. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_ids = sorted(list(g_nx.nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get node features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_attr[attr_names].reindex(node_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Stellargraph with node features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(g_nx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2110, Edges: 3720\n",
      "\n",
      " Node types:\n",
      "  paper: [2110]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [3720]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train attri2vec on Citeseer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = list(G.nodes())\n",
    "number_of_walks = 4\n",
    "length = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the UnsupervisedSampler instance with the relevant parameters passed to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervised_samples = UnsupervisedSampler(\n",
    "    G, nodes=nodes, length=length, number_of_walks=number_of_walks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the batch size and the number of epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "epochs = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an attri2vec training generator, which generates a batch of (feature of target node, index of context node, label of node pair) pairs per iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Attri2VecLinkGenerator(G, batch_size)\n",
    "train_gen = generator.flow(unsupervised_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
    "\n",
    "Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [128]\n",
    "attri2vec = Attri2Vec(\n",
    "    layer_sizes=layer_sizes, generator=generator, bias=False, normalize=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
    "x_inp, x_out = attri2vec.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the link_classification function to generate the prediction, with the 'ip' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node followed by a sigmoid activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "prediction = link_classification(\n",
    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"ip\"\n",
    ")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "    loss=keras.losses.binary_crossentropy,\n",
    "    metrics=[keras.metrics.binary_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "1351/1351 - 27s - loss: 0.6829 - binary_accuracy: 0.5572\n",
      "Epoch 2/4\n",
      "1351/1351 - 28s - loss: 0.5446 - binary_accuracy: 0.7276\n",
      "Epoch 3/4\n",
      "1351/1351 - 27s - loss: 0.3604 - binary_accuracy: 0.8649\n",
      "Epoch 4/4\n",
      "1351/1351 - 28s - loss: 0.2669 - binary_accuracy: 0.9098\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen,\n",
    "    epochs=epochs,\n",
    "    verbose=2,\n",
    "    use_multiprocessing=False,\n",
    "    workers=1,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualise Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the node based model for predicting node representations from node content attributes with the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp_src = x_inp[0]\n",
    "x_out_src = x_out[0]\n",
    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node embeddings by applying the learned mapping function to node content features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43/43 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "node_gen = Attri2VecNodeGenerator(G, batch_size).flow(node_ids)\n",
    "node_embeddings = embedding_model.predict_generator(node_gen, workers=1, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get node subjects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_targets = [node_attr[\"subject\"][node_id] for node_id in node_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the embeddings to 2d space for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = TSNE  # PCA\n",
    "\n",
    "trans = transform(n_components=2)\n",
    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the embedding points, coloring them by the target label (paper subject)\n",
    "alpha = 0.7\n",
    "label_map = {l: i for i, l in enumerate(np.unique(node_targets))}\n",
    "node_colours = [label_map[target] for target in node_targets]\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.axes().set(aspect=\"equal\")\n",
    "plt.scatter(\n",
    "    node_embeddings_2d[:, 0],\n",
    "    node_embeddings_2d[:, 1],\n",
    "    c=node_colours,\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "plt.title(\"{} visualization of node embeddings\".format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downstream Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The node representations learned by attri2vec can be used as features for downstream tasks, such as node classification, community detection, and link prediction. As attri2vec inductively learns a mapping function from node content features to the target embeddings, it has the ability to infer the representations for out-of-sample nodes, which can be used to predict the labels or links of out-of-sample nodes."
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
